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Nagaraja Kumar Pateti, Ramya Prasanthi Kota, Dr.Ch. D. V. Paradesi Rao/ International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com

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FPGA Implementation of an Algorithm to Identify a BPSK (Barker)

signal

Nagaraja Kumar Pateti1, Ramya Prasanthi Kota

2, Dr.Ch. D. V. Paradesi Rao

3

Department of Electronics & Communications Engineering, Aurora’s Engineering College,Bhongir

Abstract— Modern radars use phase modulated signals to spread their

spectrum to improve the processing gain. The Identification of the modulation format of a detected signal, the intermediate

step between signal detection and demodulation, is a majortask of an intelligent receiver, with various civilian and

military applications. Obviously, with no knowledge of thetransmitted data and many unknown parameters at the receiver,

such as the signal power, carrier frequency and phase offsets,

timing information, etc., blind identification of the modulationis a difficult task. This becomes even more challenging in

real-world scenarios.An algorithm is designed to identify the

BPSK (Barker) signal using an STFT Process andcalculating

the chip rate from the spectrum.

Keywords—Radars,ADC,Barker,STFT,Signal Identification

I. INTRODUCTION

Modern radars use phase modulated signals to spread their

spectrum to improve the processing gain. There are several

approaches of using the phase modulation. The phase changecan be π , π /2, π /4, and so forth. When the phase change is π ,it is referred to as biphase shift keying (BPSK).

Communication signals use various types of phasemodulations,this paper is mainly for BPSK because it is more

popular in radar applications. It is important to detect the

existence of a BPSK signal for an electronic warfare (EW)receiver. The detection of a BPSK signal can help identify the

radar type, which is important information. If moreinformation can be obtained from the signal, the identification

approach can be easier. In this study the requirements are to

detect the existence of a BPSK signal and find the frequencyand its chip time (orchip rate). The chip time is the shortest

time between two consecutive phase shifts in a BPSK signal

and the chip rate is the inverse of the chip time.The Identification of the modulation format of a detected

signal, the intermediate step between signal detection and

ssdemodulation, is a major task of an intelligent receiver, with

various civilian and military applications. Obviously, with

no knowledge of the transmitted data and many unknown

parameters at the receiver, such as the signal power, carrier

frequency and phase offsets, timing information, etc., blindidentification of the modulation is a difficult task. This

becomes even more challenging in real-world scenarios. So a

new method has been developed to identify the Bpsk (Barker)signal. This is achieved using an STFT Process and by

calculating the chip rate from the spectrum.

II. BARKER CODES

In 1953, R. H. Barker presented binary sequences for

synchronization purposes in telecommunications the binaryBarker sequences are finite length, discrete time sequences

with constant magnitude, and a phase ofeither φk = 0 or φk =π. The formal definition of a Barker sequence is given below

Definition

A Barker sequence is a finite length sequence A = [a0, a1, . . .

,an] of +1’s and −1’s of length n ≥ 2 such that the aperiodicautocorrelation coefficients (or side lobes)satisfies |rk| ≤ 1 for

k = 0 and similarly r−k = rk

The Barker code is a short sequence and popular for

radar applications. The Barker codes have different sequencelengths. The side lobes generated from the autocorrelation

of the Barker code have constant amplitude. The maximumlength of a Barker code is limited to 13

Consequently, a binary Barker sequence has elements

ai {−1, +1}, which are only known for lengths Nc = 2, 3, 4,

5, 7, 11, and 13.. The longest code is of length Nc=13. The

nine sequences are listed where a +1 is represented by a + anda −1 is represented by a −. It hasbeen shown that binary

Barker sequences with lengths greater than 13, with Nc odd,do not exist. Also, it has been proven that binary Barker

sequences with 4 <Nc< 1, 898, 884 with Nc even do not exist.

It has been conjectured that sequences with Nc ≥ 1, 898, 884

with Nc even also do not exist .

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Fig 1 Barker Signal

III SHORT TIME FOURIER TRANSFORM(STFT)

Short time Fourier Transform is a process, which involvesrepetitiveFFT operations for small intervals of time. The given

time period is divided into ‘n’ number of slots where ‘n’ is

the no of samples/ No of Point FFT, Therefore the time

resolution for the calculation of FFT is’ T/n’ where T is theTotal time period of the received signal.

STFT is used to know the phase and the frequency at

different instants of time so that the signal modulation type ispossible whereas in FFT the frequency and phase calculation

is not possible as it contains several peaks in a particular bin,

but the frequency or phase will be calculated at every bin

number as a result of which several frequencies and phases

are missed using an fat, This is the main reason why we havedesigned STFT process for time to frequency conversion of a

signal

IV ALGORITHM FOR THE IDENTIFICATION OF BARKER

SIGNAL

A BPSK signal is generated and the basic propertieswill be studied. Once the signal is generated STFT will be

performed on the signal. The purpose of this operation is tofind the time to frequencydomain conversion of a BPSK

(Barker) signal. The Algorithm for the identification of aBarker Signal is given below

Fig 2 Barker signal identification design flow

ALGORITHM

i. Received Bpsk (Barker) signal is converted into adigital signal using an ADC.

ii. An STFT operation is performed on the digital Bpsk

Signaliii. The amplitude (Magnitude) and the Frequency

(Index) values are processed in the analysis block.

BARKER CODE PHASE SHIFT

BARKER_2 + -

BARKER-3 + + -

BARKER-4 + + + -

BARKER-5 + + + - +

BARKER-7 + + + - - + -

BARKER-11 + + + - - - + - - + -

BARKER-13 + + + + + - - + + - - + -

Received Barker signal

Analog to Digital Converter

STFT Process

Phase code calculation

Chip Ratio calculation

Barker Order Calculation

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iv. Read the amplitude and index for each frame

v. Calculate the bin number corresponding to the peak

amplitudevi. Calculate the amplitude for the 4 bin numbers before

and after the max bin numbervii. Calculate the min/max ratio for the above bin

numbersviii. Now compare the min/max ratios with respect to the

threshold

ix. If min/max greater than threshold than phase shiftoccurs

x. The Chip rate for the Code formed from the different

frames is calculated.

xi. The signal type can be classified using the chirp rate

Fig 3 Phase shift calculation flow

The above flow chart is used to calculate the phase shift, the

inputs to this block are the magnitude(amp ) and bin number

from the STFT and the output will be the phase shift t”0” for

no phase shift and “1” for phase shift .The phase shift is calculated for every frame of data and aphase code is formed as per the flow chart show in figure 4.

Here 'i' is an integer initialized to ‘1’ and is incremented untilit is greater than ‘n’ where ‘n’ indicates no of frames (no of

code bits) .The Phase code is initialised to ‘0’ and for every

frame a phase shift (either 0 or 1) is concatenated as result of

which a complete phase code of ‘n’ bits is formed when i>ncondition is satisfied.

The order of the Barker is calculated from thederived phase code using the Chip rate using the formula

given below Barker order = no. of code bits (n) / chip rate

Where chip rate is defined as the minimum number of frames(code bits) between any two phase shifts.

Fig4 Barker identificationflow chart

V. RESULTS AND DISCUSSION

The algorithm is first modelled in Matlab to estimate the

Hardware Resources. A sample Barker signal is generated inMatlab and applied as an input to the design. The Matlab

results for the algorithm are shown below.

Enter the Frame Data

(amp,Index)

Calculate the amplitude values for 4 bin

numbers before and after the Max Bin

number

Calculate the Min/Max Ratio for all the

8 bin numbers

Calculate the sum of the ratios(Min/Max Ratio)

“SUM”

SUM>

Thres

Phase shift

Code=’1’

No Phase shiftCode=’0’

YesNo

Data (Amp, Bin number)

STFT O/P

Calculate the Phase shift

i=i+1

Display the Phase code

i >n

Concatenated with the

revious hase code

No

Yes

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The Matlab generated Signal is given as input an Analog todigital converter and thus the obtained digital samples are

given to the FPGA as an input. The algorithm consists of fifo ,

STFT operations to be performed and the obtained outputs aregiven to the Barker analysis block which classifies the type of

Barker signal . The algorithm is designed using VHDLlanguage , Synthesized in Xilinx 10.i ,Simulated in Xilinx

ISIM 10.1 i. The synthesis and simulation results are showbelow

RTL Schematic:

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Inputs

Outputs

VI CONCLUSIONS

This paper describes an algorithm for the identification of BPSK (Barker) signal which is based on the time to frequency

analysis.

DSP processors are used for implementing many of

the DSP applications. Although DSP processors areprogrammable through software, the DSP processor hardware

architecture is not flexible. Therefore, DSP processors are

limited by fixed hardware architecture such as busperformance bottlenecks, a fixed number of multiply

accumulate (MAC) blocks, fixed memory, fixed hardware

accelerator blocks, and fixed data widths. The DSP

processor’s fixed hardware architecture is not suitable forcertain applications that might require customized DSPfunctions implementations.

FPGAs provide a reconfigurable solution for

implementing DSP applications as well as higher DSPthroughput and raw data processing power than DSPprocessors. Since FPGAs can be reconfigured in hardware,

FPGAs offer complete hardware customization whileimplementing various DSP applications. Therefore, DSP

systems implemented in FPGAs can have customized

architecture, customized bus structure, customized memory,and customized hardware accelerator blocks.

So keeping in view of the advantages of using FPGAs ascompared to other customized devices this work can further beIncreased to the designing & implementation of a Barker

Identification algorithm. The generosity of the designfacilitates not only the rapid Signal Identification, but

moreover, it explores the performance of different

implementations in order to obtain the most suitable solutionfor a particular Electronic Warfare system. Some of the genericparameters ofBarker Identification are, frequency (bin number),

amplitude, chip rate, threshold, min/max ratio, sum of the

ratios,

The Above design is implemented on Xilinx Virtex-5 SX240t

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an virtex-5 Fx200t,an 8 bit ADC is used to covert the analogsignal into a digital signal the IC used is ADC08D1520,Using

these hardware the design implemented on the |Fpga and the

algorithm is verified and has given Accurate and efficientresults.

The above algorithm is an efficient algorithm, which istechnologically feasible for the fast interception of signals

without any parameter extraction (Pulse width, PulseRepetition Interval, direction of arrival). It outperforms theprevious approaches in terms of speed, sensitivity, accuracy in

the identification of a BPSK (Barker) signal.

ACKNOWLEDGMENT

The successful completion of any task would be incomplete

without the mentionof the people who made it possible

through their constant guidance and encouragement We wouldlike to express our sincere thanks and deep sense of gratitude

towards Dr.Ch D.V.Paradesi Rao for his exemplaryguidance,

monitoring and constant encouragement in research work.Thepresentationof this article could be a reality with the valuabletechnicalinput accorded from time to time.

REFERENCES

[1] Merill I. Skolnik, Introduction to RADAR Systems,3rd

ed., NewDelhi, Tata Mc Graw-Hill.[2] Phillip E. Pace, Detecting and classifying Low Probability of

Intercept Radar s, 2nd ed., Norwood, MA:Artech House, 2009. [3] Tsui, J., Digital Techniques for Wideband Receiver , 2nd ed.,Norwood, MA: Artech House, 2001.[4]Digital Channelized Receiver Based on Time-Frequency analysisfor Signal Interception.IEEE Transactions on Aerospace and

electronic systems VOL-41, No.3 JULY 2005 [5]Golomb, S.; Scholtz, R. ,Generalised Barker Sequences.IEEE Transactions on Information theory VOL-11, No.4

[6]Zhang, N.; Golomb, S.W ,On the cross correlation of

generalizedBarkersequences .IEEE Transactions on Informationtheory VOL-36, No.6

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